Ruff: apply same rules as in transformers (#2827) · zzmjohn/diffusers@1d7b4b6 · GitHub
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Ruff: apply same rules as in transformers (huggingface#2827)
* Apply same ruff settings as in transformers See https://github.com/huggingface/transformers/blob/main/pyproject.toml Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com> * Apply new style rules * Style Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com> * style * remove list, ruff wouldn't auto fix. --------- Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>
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Lines changed: 209 additions & 213 deletions

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‎examples/community/checkpoint_merger.py‎

Lines changed: 8 additions & 12 deletions

‎examples/community/imagic_stable_diffusion.py‎

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@@ -48,7 +48,7 @@
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def preprocess(image):
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w, h = image.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
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image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)

‎examples/community/lpw_stable_diffusion.py‎

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@@ -376,7 +376,7 @@ def get_weighted_text_embeddings(
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def preprocess_image(image):
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w, h = image.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
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image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
@@ -387,7 +387,7 @@ def preprocess_image(image):
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def preprocess_mask(mask, scale_factor=8):
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mask = mask.convert("L")
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w, h = mask.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
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mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
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mask = np.array(mask).astype(np.float32) / 255.0
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mask = np.tile(mask, (4, 1, 1))

‎examples/community/lpw_stable_diffusion_onnx.py‎

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@@ -403,7 +403,7 @@ def get_weighted_text_embeddings(
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def preprocess_image(image):
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w, h = image.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
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image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
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image = np.array(image).astype(np.float32) / 255.0
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image = image[None].transpose(0, 3, 1, 2)
@@ -413,7 +413,7 @@ def preprocess_image(image):
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def preprocess_mask(mask, scale_factor=8):
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mask = mask.convert("L")
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w, h = mask.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
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mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
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mask = np.array(mask).astype(np.float32) / 255.0
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mask = np.tile(mask, (4, 1, 1))

‎examples/community/stable_unclip.py‎

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@@ -46,7 +46,7 @@ def __init__(
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):
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super().__init__()
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decoder_pipe_kwargs = dict(image_encoder=None) if decoder_pipe_kwargs is None else decoder_pipe_kwargs
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decoder_pipe_kwargs = {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
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decoder_pipe_kwargs["torch_dtype"] = decoder_pipe_kwargs.get("torch_dtype", None) or prior.dtype
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‎examples/instruct_pix2pix/train_instruct_pix2pix.py‎

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@@ -673,7 +673,7 @@ def preprocess_train(examples):
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examples["edited_pixel_values"] = edited_images
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# Preprocess the captions.
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captions = [caption for caption in examples[edit_prompt_column]]
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captions = list(examples[edit_prompt_column])
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examples["input_ids"] = tokenize_captions(captions)
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return examples
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‎examples/rl/run_diffuser_locomotion.py‎

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@@ -4,17 +4,17 @@
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from diffusers.experimental import ValueGuidedRLPipeline
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config = dict(
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n_samples=64,
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horizon=32,
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num_inference_steps=20,
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n_guide_steps=2, # can set to 0 for faster sampling, does not use value network
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scale_grad_by_std=True,
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scale=0.1,
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eta=0.0,
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t_grad_cutoff=2,
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device="cpu",
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)
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config = {
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"n_samples": 64,
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"horizon": 32,
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"num_inference_steps": 20,
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"n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network
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"scale_grad_by_std": True,
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"scale": 0.1,
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"eta": 0.0,
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"t_grad_cutoff": 2,
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"device": "cpu",
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}
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if __name__ == "__main__":

‎pyproject.toml‎

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@@ -4,8 +4,8 @@ target-version = ['py37']
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[tool.ruff]
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# Never enforce `E501` (line length violations).
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ignore = ["E501", "E741", "W605"]
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select = ["E", "F", "I", "W"]
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ignore = ["C901", "E501", "E741", "W605"]
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select = ["C", "E", "F", "I", "W"]
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line-length = 119
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# Ignore import violations in all `__init__.py` files.

‎scripts/convert_ddpm_original_checkpoint_to_diffusers.py‎

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@@ -404,7 +404,7 @@ def convert_vq_autoenc_checkpoint(checkpoint, config):
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config = json.loads(f.read())
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# unet case
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key_prefix_set = set(key.split(".")[0] for key in checkpoint.keys())
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key_prefix_set = {key.split(".")[0] for key in checkpoint.keys()}
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if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
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converted_checkpoint = convert_vq_autoenc_checkpoint(checkpoint, config)
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else:

‎scripts/convert_models_diffuser_to_diffusers.py‎

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